Upon any nonstationary time series of any dimensions, abrupt distributional changes are ubiquitous patterns of great interest found in many sciences involving with evolving systems. Change points as temporal locations of such occurrences and their multiplicity are key parts of deterministic structures of the time series under study. Nowadays, change-point analysis has well recognized in statistics literature and beyond as an essential scientific methodology that aims to detect change points on the time-ordered observations and then partition the whole time series into homogeneously distributional segments. Change-point analysis can be traced back to 1950s ([1]}; [2]}; [3]}). So far, it has been playing a crucial role in diverse fields including bioinformatics ([4]}; [5]}), behavioural science([6]}; [7]}), neuroimage ([8]}), climate science ([9]}), finance ([10]}), and speech recognition ([11]}).
